Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods

Mao, Yimin; Mwakapesa, Deborah Simon; Xu, Kaibin; Nanehkaran, Yaser Ahangari; Zhang, Maosheng

doi:10.1007/s11629-020-6491-7

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…This task is developed by the DBSCAN algorithm. Unlike most clustering algorithms, DBSCAN uses local connectivity and density functions to perform the clustering procedure, which it is a advantage because it does not require cluster initialization [ 32 ]. For the use of DBSCAN in the present study, it should be assumed that every superpixel is described by its centroid, as shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Efficient System for Delimitation of Benign and Malignant Breast Masses

Mújica-Vargas

Matuz-Cruz

García-Aquino

et al. 2022

Entropy

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In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses’ regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were JSC≥0.907, DM≥0.913, HD≥7.025, and MCR≤6.431 for benign masses and JSC≥0.897, DM≥0.900, HD≥8.666, and MCR≤8.016 for malignant ones, while the MID dataset resulted in JSC≥0.890, DM≥0.905, HD≥8.370, and MCR≤7.241 along with JSC≥0.881, DM≥0.898, HD≥8.865, and MCR≤7.808 for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.

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Section: Methodsmentioning

confidence: 99%

Efficient System for Delimitation of Benign and Malignant Breast Masses

Mújica-Vargas

Matuz-Cruz

García-Aquino

et al. 2022

Entropy

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“…Generally, clustering can be categorized into partition-based methods, hierarchical methods, density-based methods, and model-based clustering methods. Based on these categories, various clustering methods have been proposed for conducting LSM modeling [41][42][43][44][45][46][47]. However, from the literature review, it can be noted that these methods are rare in the field of LSM compared to supervised learning-based methods; at present, there is no agreement on the most suitable method for LSM [48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

et al. 2023

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Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be applied when the data has no labels and when there is insufficient inventory data. First, based on historical records, survey reports, and previous studies, 293 landslides were mapped in the study area and 7 landslide-influencing attributes were selected for modeling. Second, the methods were clustered in the study area mapping units into 469 and 476 subsets, respectively; for mapping landslide susceptibility, the subsets were classified into 5 susceptibility levels through the K-means method using landslide densities and attribute values. Then, their performances were assessed and compared using statistical metrics and the receiver operating curve (ROC). The outcomes indicated that similarity measures influenced the accuracy and the predictive power of these clustering models. In particular, when using a link-based similarity measure, the ROCK performed better with overall performance accuracy of 0.8933 and an area under the curve (AUC) of 0.875. The maps constructed from the models can be useful in landslide assessment, prevention, and mitigation strategies in the study area, especially for areas classified with higher susceptibility levels. Moreover, this comparison provides a new perspective in the selection of a considerable model for LSM in the Baota District.

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“…Because of these limitations, SL methods may not be applicable where there are a limited number of labeled samples as they are not always easy to obtain and may be expensive to acquire in abundance through image interpretation and site surveying, especially in a large study area. USL-based approaches are applied and have contributed to improving the implementation and the accuracy of LSM in such situations (Lei et al, 2018;Hu et al, 2021;Yimin et al, 2021;Mao et al, 2022;Su et al, 2022;Liu et al, 2023). USL-based methods such as clustering can be used to map the susceptibility areas, as they can identify the underlying structures in unlabeled datasets, hence, do not require data with predefined labels, and do not involve a training process during their implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Over decades, these methods have been widely used in other fields such as marketing research, pattern recognition, and image processing, but very rarely explored in LSM studies (Huang et al, 2020;Su et al, 2022). In recent years, making use of the advantages of these methods, some landslide researchers have also shown interest and conducted LSM studies using these methods (Wan et al, 2015;Wang et al, 2017;Hu et al, 2019;Mao et al, 2021a;Mao et al, 2021b;Hu et al, 2021;Pokharel et al, 2021;Yimin et al, 2021;Mao et al, 2022). From the analysis of these studies and other traditional clustering algorithms, some limitations were observed: the inability to detect subclasses with arbitrary shapes, sensitivity to noise, inability to perform well in large study areas with large datasets, and principally a standard method to process the uncertain data (rainfall) has not being obtained yet.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Mwakapesa

Lan²,

Nanehkaran³

et al. 2023

Front. Environ. Sci.

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Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

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Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods

Cited by 10 publications

References 46 publications

Efficient System for Delimitation of Benign and Malignant Breast Masses

Efficient System for Delimitation of Benign and Malignant Breast Masses

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

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